Predictive Analytics for the Benefits, Risks of Collaborative Shipping

As fuel and transportation costs continue to rise and cut into profit margins, a growing number of consumer packaged goods (CPGs) companies such as Colgate-Palmolive and Nestle S.A. are piloting shared intermodal rail service in specific geographies to reduce costs as well as carbon dioxide emissions.

Under an arrangement announced by Colgate-Palmolive, Mondelez International and Nestle, each company loads containers at their respective factories in Poland where they are then transported by rail and ferry before being trucked to each company’s logistics providers, eventually reaching The Netherlands.

Shipping can be very expensive for enterprise CPG firms but it becomes even more convoluted for mid-size companies.As retailers move to more just-in-time fulfillment strategies, CPG manufacturers have to adjust their own fulfillment and transportation operations – often at greater expense and risk.

The situation is particularly difficult for mid-size CPG companies that lack the volume and resources of enterprise companies, according to a recent report.

CPG businesses of all sizes are increasingly using predictive analytics to identify opportunities to better manage and optimize their transportation operations.

For instance, refrigerated trucking companies are turning to analytics to reduce delivery times and maximize trailer capacity, notes a recent article by New Century Transportation. Meanwhile, company leaders are able to identify the best routes for drivers by evaluating travel time, traffic patterns, fuel costs, and other variables.

CPG companies can similarly apply analytics to identify the costs to transport goods by train, ship, truck, air, etc., as well as where it may make sense to partner with other CPG firms to share intermodal transportation services to reduce costs and improve operational efficiencies.

As part of these efforts, CPG executives also need to evaluate the potential risks associated with shared transportation initiatives with other companies and even rivals.

For instance, what are the risks of inventory between partners becoming mixed or damaged during different stages of distribution? What are the greatest inherent logistical challenges associated with transitioning from truck to rail transportation and can those challenges be dealt with cost-effectively?

What are the time-to-market considerations with shared transportation initiatives? Are legacy supply chain systems capable of monitoring and tracking cargo in shared containers effectively? Are the risks of cargo theft greater in a collaborative venture than when handled independently?

Predictive analytics can also help business leaders of CPG firms identify and weigh the anticipated benefits of shared transportation efforts against the potential risks.

For instance, CPG companies that use a “less than container load” (LCL) approach to sharing ocean-bound cargo shipping freight containers with one or more companies only pay for the space used in the container and not for the entire container.

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